Overview

Dataset statistics

Number of variables18
Number of observations5329
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory713.1 KiB
Average record size in memory137.0 B

Variable types

Text4
Boolean1
Categorical3
Numeric10

Alerts

EN(A) is highly overall correlated with r(AVI)(Ã…) and 2 other fieldsHigh correlation
EN(B) is highly overall correlated with r(BVI)(Ã…) and 2 other fieldsHigh correlation
l(A-O)(Ã…) is highly overall correlated with r(AVI)(Ã…) and 2 other fieldsHigh correlation
l(B-O)(Ã…) is highly overall correlated with r(BVI)(Ã…) and 2 other fieldsHigh correlation
r(AVI)(Ã…) is highly overall correlated with EN(A) and 3 other fieldsHigh correlation
r(AXII)(Ã…) is highly overall correlated with EN(A) and 3 other fieldsHigh correlation
r(BVI)(Ã…) is highly overall correlated with EN(B) and 4 other fieldsHigh correlation
tG is highly overall correlated with EN(A) and 5 other fieldsHigh correlation
v(A) is highly overall correlated with v(B)High correlation
v(B) is highly overall correlated with v(A)High correlation
ΔENR is highly overall correlated with EN(B) and 3 other fieldsHigh correlation
μ is highly overall correlated with EN(B) and 4 other fieldsHigh correlation
In literature is highly imbalanced (75.0%)Imbalance
Compound has unique valuesUnique
l(A-O)(Ã…) has 365 (6.8%) zerosZeros
l(B-O)(Ã…) has 365 (6.8%) zerosZeros

Reproduction

Analysis started2023-12-05 12:32:35.682016
Analysis finished2023-12-05 12:32:44.450704
Duration8.77 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Compound
Text

UNIQUE 

Distinct5329
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
2023-12-05T20:32:44.576797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.8230437
Min length4

Characters and Unicode

Total characters31031
Distinct characters44
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5329 ?
Unique (%)100.0%

Sample

1st rowAc2O3
2nd rowAcAgO3
3rd rowAcAlO3
4th rowAcAsO3
5th rowAcAuO3
ValueCountFrequency (%)
bauo3 2
 
< 0.1%
beuo3 2
 
< 0.1%
ac2o3 1
 
< 0.1%
acceo3 1
 
< 0.1%
acbo3 1
 
< 0.1%
acbao3 1
 
< 0.1%
acbeo3 1
 
< 0.1%
acbio3 1
 
< 0.1%
accao3 1
 
< 0.1%
accdo3 1
 
< 0.1%
Other values (5317) 5317
99.8%
2023-12-05T20:32:44.861148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 5474
17.6%
3 5329
17.2%
T 1160
 
3.7%
C 1015
 
3.3%
a 1015
 
3.3%
P 1015
 
3.3%
e 870
 
2.8%
b 870
 
2.8%
S 870
 
2.8%
r 870
 
2.8%
Other values (34) 12543
40.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15914
51.3%
Lowercase Letter 9715
31.3%
Decimal Number 5402
 
17.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 5474
34.4%
T 1160
 
7.3%
C 1015
 
6.4%
P 1015
 
6.4%
S 870
 
5.5%
N 725
 
4.6%
A 725
 
4.6%
R 580
 
3.6%
B 580
 
3.6%
G 435
 
2.7%
Other values (13) 3335
21.0%
Lowercase Letter
ValueCountFrequency (%)
a 1015
10.4%
e 870
 
9.0%
b 870
 
9.0%
r 870
 
9.0%
u 870
 
9.0%
i 725
 
7.5%
d 580
 
6.0%
n 580
 
6.0%
o 435
 
4.5%
m 435
 
4.5%
Other values (9) 2465
25.4%
Decimal Number
ValueCountFrequency (%)
3 5329
98.6%
2 73
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 25629
82.6%
Common 5402
 
17.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 5474
21.4%
T 1160
 
4.5%
C 1015
 
4.0%
a 1015
 
4.0%
P 1015
 
4.0%
e 870
 
3.4%
b 870
 
3.4%
S 870
 
3.4%
r 870
 
3.4%
u 870
 
3.4%
Other values (32) 11600
45.3%
Common
ValueCountFrequency (%)
3 5329
98.6%
2 73
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 5474
17.6%
3 5329
17.2%
T 1160
 
3.7%
C 1015
 
3.3%
a 1015
 
3.3%
P 1015
 
3.3%
e 870
 
2.8%
b 870
 
2.8%
S 870
 
2.8%
r 870
 
2.8%
Other values (34) 12543
40.4%

A
Text

Distinct73
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
2023-12-05T20:32:45.009912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9178082
Min length1

Characters and Unicode

Total characters10220
Distinct characters42
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAc
2nd rowAc
3rd rowAc
4th rowAc
5th rowAc
ValueCountFrequency (%)
ac 73
 
1.4%
ag 73
 
1.4%
al 73
 
1.4%
as 73
 
1.4%
au 73
 
1.4%
b 73
 
1.4%
ba 73
 
1.4%
be 73
 
1.4%
bi 73
 
1.4%
ca 73
 
1.4%
Other values (63) 4599
86.3%
2023-12-05T20:32:45.238914image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 584
 
5.7%
C 511
 
5.0%
P 511
 
5.0%
a 511
 
5.0%
S 438
 
4.3%
b 438
 
4.3%
u 438
 
4.3%
r 438
 
4.3%
e 438
 
4.3%
i 365
 
3.6%
Other values (32) 5548
54.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5329
52.1%
Lowercase Letter 4891
47.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 584
 
11.0%
C 511
 
9.6%
P 511
 
9.6%
S 438
 
8.2%
A 365
 
6.8%
N 365
 
6.8%
R 292
 
5.5%
B 292
 
5.5%
G 219
 
4.1%
H 219
 
4.1%
Other values (13) 1533
28.8%
Lowercase Letter
ValueCountFrequency (%)
a 511
10.4%
b 438
 
9.0%
u 438
 
9.0%
r 438
 
9.0%
e 438
 
9.0%
i 365
 
7.5%
d 292
 
6.0%
n 292
 
6.0%
g 219
 
4.5%
s 219
 
4.5%
Other values (9) 1241
25.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 10220
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 584
 
5.7%
C 511
 
5.0%
P 511
 
5.0%
a 511
 
5.0%
S 438
 
4.3%
b 438
 
4.3%
u 438
 
4.3%
r 438
 
4.3%
e 438
 
4.3%
i 365
 
3.6%
Other values (32) 5548
54.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 584
 
5.7%
C 511
 
5.0%
P 511
 
5.0%
a 511
 
5.0%
S 438
 
4.3%
b 438
 
4.3%
u 438
 
4.3%
r 438
 
4.3%
e 438
 
4.3%
i 365
 
3.6%
Other values (32) 5548
54.3%

B
Text

Distinct73
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
2023-12-05T20:32:45.377682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9178082
Min length1

Characters and Unicode

Total characters10220
Distinct characters42
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAc
2nd rowAg
3rd rowAl
4th rowAs
5th rowAu
ValueCountFrequency (%)
ac 73
 
1.4%
ag 73
 
1.4%
al 73
 
1.4%
as 73
 
1.4%
au 73
 
1.4%
b 73
 
1.4%
ba 73
 
1.4%
be 73
 
1.4%
bi 73
 
1.4%
ca 73
 
1.4%
Other values (63) 4599
86.3%
2023-12-05T20:32:45.606386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 584
 
5.7%
C 511
 
5.0%
P 511
 
5.0%
a 511
 
5.0%
S 438
 
4.3%
b 438
 
4.3%
u 438
 
4.3%
r 438
 
4.3%
e 438
 
4.3%
i 365
 
3.6%
Other values (32) 5548
54.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5329
52.1%
Lowercase Letter 4891
47.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 584
 
11.0%
C 511
 
9.6%
P 511
 
9.6%
S 438
 
8.2%
A 365
 
6.8%
N 365
 
6.8%
R 292
 
5.5%
B 292
 
5.5%
G 219
 
4.1%
H 219
 
4.1%
Other values (13) 1533
28.8%
Lowercase Letter
ValueCountFrequency (%)
a 511
10.4%
b 438
 
9.0%
u 438
 
9.0%
r 438
 
9.0%
e 438
 
9.0%
i 365
 
7.5%
d 292
 
6.0%
n 292
 
6.0%
g 219
 
4.5%
s 219
 
4.5%
Other values (9) 1241
25.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 10220
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 584
 
5.7%
C 511
 
5.0%
P 511
 
5.0%
a 511
 
5.0%
S 438
 
4.3%
b 438
 
4.3%
u 438
 
4.3%
r 438
 
4.3%
e 438
 
4.3%
i 365
 
3.6%
Other values (32) 5548
54.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 584
 
5.7%
C 511
 
5.0%
P 511
 
5.0%
a 511
 
5.0%
S 438
 
4.3%
b 438
 
4.3%
u 438
 
4.3%
r 438
 
4.3%
e 438
 
4.3%
i 365
 
3.6%
Other values (32) 5548
54.3%

In literature
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
False
5107 
True
 
222
ValueCountFrequency (%)
False 5107
95.8%
True 222
 
4.2%
2023-12-05T20:32:45.697475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

v(A)
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
-
1881 
0
1233 
3
935 
2
884 
1
222 
Other values (2)
 
174

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5329
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
- 1881
35.3%
0 1233
23.1%
3 935
17.5%
2 884
16.6%
1 222
 
4.2%
4 146
 
2.7%
5 28
 
0.5%

Length

2023-12-05T20:32:45.777683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T20:32:45.860962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1881
35.3%
0 1233
23.1%
3 935
17.5%
2 884
16.6%
1 222
 
4.2%
4 146
 
2.7%
5 28
 
0.5%

Most occurring characters

ValueCountFrequency (%)
- 1881
35.3%
0 1233
23.1%
3 935
17.5%
2 884
16.6%
1 222
 
4.2%
4 146
 
2.7%
5 28
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3448
64.7%
Dash Punctuation 1881
35.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1233
35.8%
3 935
27.1%
2 884
25.6%
1 222
 
6.4%
4 146
 
4.2%
5 28
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 1881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5329
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1881
35.3%
0 1233
23.1%
3 935
17.5%
2 884
16.6%
1 222
 
4.2%
4 146
 
2.7%
5 28
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1881
35.3%
0 1233
23.1%
3 935
17.5%
2 884
16.6%
1 222
 
4.2%
4 146
 
2.7%
5 28
 
0.5%

v(B)
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
-
1881 
0
1233 
3
935 
4
884 
5
222 
Other values (2)
 
174

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5329
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
- 1881
35.3%
0 1233
23.1%
3 935
17.5%
4 884
16.6%
5 222
 
4.2%
2 146
 
2.7%
1 28
 
0.5%

Length

2023-12-05T20:32:45.960952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T20:32:46.041412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1881
35.3%
0 1233
23.1%
3 935
17.5%
4 884
16.6%
5 222
 
4.2%
2 146
 
2.7%
1 28
 
0.5%

Most occurring characters

ValueCountFrequency (%)
- 1881
35.3%
0 1233
23.1%
3 935
17.5%
4 884
16.6%
5 222
 
4.2%
2 146
 
2.7%
1 28
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3448
64.7%
Dash Punctuation 1881
35.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1233
35.8%
3 935
27.1%
4 884
25.6%
5 222
 
6.4%
2 146
 
4.2%
1 28
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 1881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5329
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1881
35.3%
0 1233
23.1%
3 935
17.5%
4 884
16.6%
5 222
 
4.2%
2 146
 
2.7%
1 28
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1881
35.3%
0 1233
23.1%
3 935
17.5%
4 884
16.6%
5 222
 
4.2%
2 146
 
2.7%
1 28
 
0.5%

r(AXII)(Ã…)
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97465566
Minimum0.27
Maximum1.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-12-05T20:32:46.253799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.27
5-th percentile0.54
Q10.69
median0.93
Q31.18
95-th percentile1.64
Maximum1.88
Range1.61
Interquartile range (IQR)0.49

Descriptive statistics

Standard deviation0.33713942
Coefficient of variation (CV)0.34590618
Kurtosis-0.23420681
Mean0.97465566
Median Absolute Deviation (MAD)0.25
Skewness0.45494584
Sum5193.94
Variance0.11366299
MonotonicityNot monotonic
2023-12-05T20:32:46.368084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.57 182
 
3.4%
0.68 175
 
3.3%
0.92 174
 
3.3%
1.12 171
 
3.2%
1.14 168
 
3.2%
0.89 146
 
2.7%
0.96 143
 
2.7%
0.67 123
 
2.3%
1.08 123
 
2.3%
1.34 121
 
2.3%
Other values (65) 3803
71.4%
ValueCountFrequency (%)
0.27 73
1.4%
0.4 73
1.4%
0.45 73
1.4%
0.52 23
 
0.4%
0.54 74
1.4%
0.57 182
3.4%
0.58 63
 
1.2%
0.59 49
 
0.9%
0.6 76
1.4%
0.61 59
 
1.1%
ValueCountFrequency (%)
1.88 73
1.4%
1.72 73
1.4%
1.7 58
1.1%
1.64 73
1.4%
1.61 73
1.4%
1.49 72
1.4%
1.48 48
0.9%
1.44 73
1.4%
1.39 73
1.4%
1.38 73
1.4%

r(AVI)(Ã…)
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82561681
Minimum0.27
Maximum1.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-12-05T20:32:46.474225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.27
5-th percentile0.52
Q10.63
median0.8
Q30.95
95-th percentile1.35
Maximum1.67
Range1.4
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.24500016
Coefficient of variation (CV)0.29674803
Kurtosis1.4672113
Mean0.82561681
Median Absolute Deviation (MAD)0.16
Skewness0.86552976
Sum4399.712
Variance0.060025079
MonotonicityNot monotonic
2023-12-05T20:32:46.590692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.62 292
 
5.5%
0.63 292
 
5.5%
0.68 292
 
5.5%
0.95 219
 
4.1%
0.9 219
 
4.1%
0.94 219
 
4.1%
0.99 146
 
2.7%
0.96 146
 
2.7%
0.72 146
 
2.7%
0.86 146
 
2.7%
Other values (41) 3212
60.3%
ValueCountFrequency (%)
0.27 73
1.4%
0.4 73
1.4%
0.45 73
1.4%
0.52 73
1.4%
0.54 73
1.4%
0.57 146
2.7%
0.58 73
1.4%
0.59 73
1.4%
0.6 73
1.4%
0.61 73
1.4%
ValueCountFrequency (%)
1.67 73
1.4%
1.52 73
1.4%
1.38 73
1.4%
1.35 73
1.4%
1.2 73
1.4%
1.18 73
1.4%
1.12 73
1.4%
1.11 73
1.4%
1.06 73
1.4%
1.032 1
 
< 0.1%

r(BVI)(Ã…)
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81784481
Minimum0.27
Maximum1.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-12-05T20:32:46.709989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.27
5-th percentile0.52
Q10.63
median0.77
Q30.95
95-th percentile1.35
Maximum1.67
Range1.4
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.24747959
Coefficient of variation (CV)0.30259969
Kurtosis1.4404699
Mean0.81784481
Median Absolute Deviation (MAD)0.16
Skewness0.87236318
Sum4358.295
Variance0.061246147
MonotonicityNot monotonic
2023-12-05T20:32:46.821055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.62 222
 
4.2%
0.95 190
 
3.6%
0.9 183
 
3.4%
0.94 173
 
3.2%
0.72 172
 
3.2%
0.76 170
 
3.2%
0.68 167
 
3.1%
0.86 153
 
2.9%
0.6 153
 
2.9%
0.63 147
 
2.8%
Other values (58) 3599
67.5%
ValueCountFrequency (%)
0.27 73
1.4%
0.33 1
 
< 0.1%
0.4 73
1.4%
0.45 73
1.4%
0.46 8
 
0.2%
0.48 23
 
0.4%
0.49 13
 
0.2%
0.52 28
 
0.5%
0.53 97
1.8%
0.535 1
 
< 0.1%
ValueCountFrequency (%)
1.67 73
1.4%
1.52 73
1.4%
1.38 73
1.4%
1.35 73
1.4%
1.22 4
 
0.1%
1.2 50
0.9%
1.18 73
1.4%
1.15 3
 
0.1%
1.12 73
1.4%
1.11 66
1.2%

EN(A)
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5724658
Minimum0.79
Maximum2.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-12-05T20:32:46.937091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.79
5-th percentile0.89
Q11.2
median1.55
Q31.93
95-th percentile2.28
Maximum2.54
Range1.75
Interquartile range (IQR)0.73

Descriptive statistics

Standard deviation0.44924779
Coefficient of variation (CV)0.28569639
Kurtosis-1.0989461
Mean1.5724658
Median Absolute Deviation (MAD)0.35
Skewness0.17110237
Sum8379.67
Variance0.20182357
MonotonicityNot monotonic
2023-12-05T20:32:47.045061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9 292
 
5.5%
2.2 292
 
5.5%
1.2 219
 
4.1%
1.1 219
 
4.1%
2.28 146
 
2.7%
0.82 146
 
2.7%
1.36 146
 
2.7%
1.3 146
 
2.7%
1.5 146
 
2.7%
1.22 146
 
2.7%
Other values (46) 3431
64.4%
ValueCountFrequency (%)
0.79 73
 
1.4%
0.82 146
2.7%
0.89 73
 
1.4%
0.93 73
 
1.4%
0.95 73
 
1.4%
0.98 73
 
1.4%
1 73
 
1.4%
1.1 219
4.1%
1.12 73
 
1.4%
1.13 146
2.7%
ValueCountFrequency (%)
2.54 73
 
1.4%
2.36 73
 
1.4%
2.33 73
 
1.4%
2.28 146
2.7%
2.2 292
5.5%
2.18 73
 
1.4%
2.16 73
 
1.4%
2.1 73
 
1.4%
2.05 73
 
1.4%
2.04 73
 
1.4%

EN(B)
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5724658
Minimum0.79
Maximum2.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-12-05T20:32:47.157773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.79
5-th percentile0.89
Q11.2
median1.55
Q31.93
95-th percentile2.28
Maximum2.54
Range1.75
Interquartile range (IQR)0.73

Descriptive statistics

Standard deviation0.44924779
Coefficient of variation (CV)0.28569639
Kurtosis-1.0989461
Mean1.5724658
Median Absolute Deviation (MAD)0.35
Skewness0.17110237
Sum8379.67
Variance0.20182357
MonotonicityNot monotonic
2023-12-05T20:32:47.271724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9 292
 
5.5%
2.2 292
 
5.5%
1.2 219
 
4.1%
1.1 219
 
4.1%
2.28 146
 
2.7%
0.82 146
 
2.7%
1.36 146
 
2.7%
1.3 146
 
2.7%
1.5 146
 
2.7%
1.22 146
 
2.7%
Other values (46) 3431
64.4%
ValueCountFrequency (%)
0.79 73
 
1.4%
0.82 146
2.7%
0.89 73
 
1.4%
0.93 73
 
1.4%
0.95 73
 
1.4%
0.98 73
 
1.4%
1 73
 
1.4%
1.1 219
4.1%
1.12 73
 
1.4%
1.13 146
2.7%
ValueCountFrequency (%)
2.54 73
 
1.4%
2.36 73
 
1.4%
2.33 73
 
1.4%
2.28 146
2.7%
2.2 292
5.5%
2.18 73
 
1.4%
2.16 73
 
1.4%
2.1 73
 
1.4%
2.05 73
 
1.4%
2.04 73
 
1.4%

l(A-O)(Ã…)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2897999
Minimum0
Maximum3.3001759
Zeros365
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-12-05T20:32:47.377447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.2906436
median2.3934435
Q32.5718122
95-th percentile2.9429989
Maximum3.3001759
Range3.3001759
Interquartile range (IQR)0.28116853

Descriptive statistics

Standard deviation0.66423858
Coefficient of variation (CV)0.29008587
Kurtosis6.8510543
Mean2.2897999
Median Absolute Deviation (MAD)0.16025573
Skewness-2.6596696
Sum12202.343
Variance0.44121289
MonotonicityNot monotonic
2023-12-05T20:32:47.499991image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 365
 
6.8%
2.478196261 73
 
1.4%
2.417029414 73
 
1.4%
2.361078957 73
 
1.4%
2.316806575 73
 
1.4%
2.332748705 73
 
1.4%
2.33939643 73
 
1.4%
3.112266446 73
 
1.4%
2.422516721 73
 
1.4%
2.759925005 73
 
1.4%
Other values (59) 4307
80.8%
ValueCountFrequency (%)
0 365
6.8%
1.891523329 73
 
1.4%
2.062330776 73
 
1.4%
2.079035521 73
 
1.4%
2.082800509 73
 
1.4%
2.106036534 73
 
1.4%
2.174450466 73
 
1.4%
2.178341873 73
 
1.4%
2.186788809 73
 
1.4%
2.214684508 73
 
1.4%
ValueCountFrequency (%)
3.300175886 73
1.4%
3.112266446 73
1.4%
3.025719299 73
1.4%
2.942998888 73
1.4%
2.768350558 73
1.4%
2.76145322 73
1.4%
2.759925005 73
1.4%
2.689118757 73
1.4%
2.675987786 73
1.4%
2.659877134 73
1.4%

l(B-O)(Ã…)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0125874
Minimum0
Maximum3.0097472
Zeros365
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-12-05T20:32:47.628854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.9568083
median2.0961408
Q32.2864352
95-th percentile2.6439007
Maximum3.0097472
Range3.0097472
Interquartile range (IQR)0.32962692

Descriptive statistics

Standard deviation0.59850858
Coefficient of variation (CV)0.29738265
Kurtosis6.0137726
Mean2.0125874
Median Absolute Deviation (MAD)0.16391372
Skewness-2.4117203
Sum10725.078
Variance0.35821252
MonotonicityNot monotonic
2023-12-05T20:32:47.733845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 365
 
6.8%
2.226583834 73
 
1.4%
2.074614707 73
 
1.4%
2.075848892 73
 
1.4%
2.018753288 73
 
1.4%
2.001424352 73
 
1.4%
1.98797081 73
 
1.4%
2.825996661 73
 
1.4%
2.64390067 73
 
1.4%
2.425828064 73
 
1.4%
Other values (59) 4307
80.8%
ValueCountFrequency (%)
0 365
6.8%
1.624661665 73
 
1.4%
1.7456 73
 
1.4%
1.758039163 73
 
1.4%
1.783360927 73
 
1.4%
1.7893 73
 
1.4%
1.803349248 73
 
1.4%
1.892894405 73
 
1.4%
1.899964183 73
 
1.4%
1.927848949 73
 
1.4%
ValueCountFrequency (%)
3.009747218 73
1.4%
2.825996661 73
1.4%
2.723507129 73
1.4%
2.64390067 73
1.4%
2.64009357 73
1.4%
2.488353231 73
1.4%
2.452968591 73
1.4%
2.425828064 73
1.4%
2.376509378 73
1.4%
2.362863567 73
1.4%

ΔENR
Real number (ℝ)

HIGH CORRELATION 

Distinct4912
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.1999934
Minimum-5.4115357
Maximum-0.60171429
Zeros0
Zeros (%)0.0%
Negative5329
Negative (%)100.0%
Memory size41.8 KiB
2023-12-05T20:32:47.843955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-5.4115357
5-th percentile-3.4272214
Q1-2.6048214
median-2.1011786
Q3-1.7115
95-th percentile-1.3202286
Maximum-0.60171429
Range4.8098214
Interquartile range (IQR)0.89332143

Descriptive statistics

Standard deviation0.66846517
Coefficient of variation (CV)-0.30384871
Kurtosis1.067129
Mean-2.1999934
Median Absolute Deviation (MAD)0.43375
Skewness-0.85050407
Sum-11723.765
Variance0.44684568
MonotonicityNot monotonic
2023-12-05T20:32:47.961673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.519 5
 
0.1%
-1.729357143 5
 
0.1%
-1.6305 4
 
0.1%
-2.052642857 4
 
0.1%
-1.104857143 4
 
0.1%
-1.826821429 3
 
0.1%
-1.643428571 3
 
0.1%
-1.687285714 3
 
0.1%
-2.417714286 3
 
0.1%
-1.801785714 3
 
0.1%
Other values (4902) 5292
99.3%
ValueCountFrequency (%)
-5.411535714 1
< 0.1%
-5.242214286 1
< 0.1%
-5.1665 1
< 0.1%
-5.096357143 1
< 0.1%
-5.065142857 1
< 0.1%
-4.899678571 1
< 0.1%
-4.899142857 1
< 0.1%
-4.864285714 1
< 0.1%
-4.824285714 1
< 0.1%
-4.788428571 1
< 0.1%
ValueCountFrequency (%)
-0.601714286 1
< 0.1%
-0.680214286 1
< 0.1%
-0.736285714 1
< 0.1%
-0.737035714 1
< 0.1%
-0.743214286 1
< 0.1%
-0.750214286 1
< 0.1%
-0.765857143 1
< 0.1%
-0.770214286 1
< 0.1%
-0.778178571 1
< 0.1%
-0.778571429 2
< 0.1%

tG
Real number (ℝ)

HIGH CORRELATION 

Distinct2936
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76609049
Minimum0.38464766
Maximum1.3888085
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-12-05T20:32:48.068069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.38464766
5-th percentile0.56454853
Q10.66853732
median0.75643981
Q30.85062845
95-th percentile1.0063963
Maximum1.3888085
Range1.0041609
Interquartile range (IQR)0.18209113

Descriptive statistics

Standard deviation0.13628032
Coefficient of variation (CV)0.17789063
Kurtosis0.17276309
Mean0.76609049
Median Absolute Deviation (MAD)0.091433597
Skewness0.43119718
Sum4082.4962
Variance0.018572326
MonotonicityNot monotonic
2023-12-05T20:32:48.178806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.707106781 74
 
1.4%
0.942809042 12
 
0.2%
0.759485061 11
 
0.2%
0.628539361 11
 
0.2%
0.824957911 9
 
0.2%
0.689604138 9
 
0.2%
0.882133212 9
 
0.2%
0.60565233 9
 
0.2%
0.848528137 9
 
0.2%
0.693765144 8
 
0.2%
Other values (2926) 5168
97.0%
ValueCountFrequency (%)
0.384647663 1
< 0.1%
0.40440696 1
< 0.1%
0.414590295 1
< 0.1%
0.424772779 1
< 0.1%
0.426106692 1
< 0.1%
0.429406663 1
< 0.1%
0.435887742 1
< 0.1%
0.442229648 1
< 0.1%
0.446836207 1
< 0.1%
0.447995735 1
< 0.1%
ValueCountFrequency (%)
1.388808528 1
< 0.1%
1.321061771 1
< 0.1%
1.312593426 1
< 0.1%
1.28850569 1
< 0.1%
1.287188392 1
< 0.1%
1.274485875 1
< 0.1%
1.253681212 1
< 0.1%
1.225651754 1
< 0.1%
1.223675807 1
< 0.1%
1.219441635 1
< 0.1%

Ï„
Text

Distinct1608
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
2023-12-05T20:32:48.308987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length12
Median length1
Mean length5.1578157
Min length1

Characters and Unicode

Total characters27486
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1152 ?
Unique (%)21.6%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
3143
59.0%
8.829629905 6
 
0.1%
5.345872478 6
 
0.1%
4.930235309 6
 
0.1%
4.827582385 5
 
0.1%
7.17810702 5
 
0.1%
4.25823497 5
 
0.1%
7.245495609 5
 
0.1%
5.055319682 5
 
0.1%
18.00286278 4
 
0.1%
Other values (1598) 2139
40.1%
2023-12-05T20:32:48.530755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 3657
13.3%
1 2504
9.1%
4 2331
8.5%
3 2281
8.3%
2 2249
8.2%
5 2199
8.0%
. 2186
8.0%
8 2145
7.8%
6 2073
7.5%
7 2032
7.4%
Other values (2) 3829
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21643
78.7%
Dash Punctuation 3657
 
13.3%
Other Punctuation 2186
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2504
11.6%
4 2331
10.8%
3 2281
10.5%
2 2249
10.4%
5 2199
10.2%
8 2145
9.9%
6 2073
9.6%
7 2032
9.4%
9 2013
9.3%
0 1816
8.4%
Dash Punctuation
ValueCountFrequency (%)
- 3657
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27486
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 3657
13.3%
1 2504
9.1%
4 2331
8.5%
3 2281
8.3%
2 2249
8.2%
5 2199
8.0%
. 2186
8.0%
8 2145
7.8%
6 2073
7.5%
7 2032
7.4%
Other values (2) 3829
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27486
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 3657
13.3%
1 2504
9.1%
4 2331
8.5%
3 2281
8.3%
2 2249
8.2%
5 2199
8.0%
. 2186
8.0%
8 2145
7.8%
6 2073
7.5%
7 2032
7.4%
Other values (2) 3829
13.9%

μ
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58417554
Minimum0.19285714
Maximum1.1928571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2023-12-05T20:32:48.642773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.19285714
5-th percentile0.37142857
Q10.45
median0.55
Q30.67857143
95-th percentile0.96428571
Maximum1.1928571
Range1
Interquartile range (IQR)0.22857143

Descriptive statistics

Standard deviation0.17677038
Coefficient of variation (CV)0.30259805
Kurtosis1.4405109
Mean0.58417554
Median Absolute Deviation (MAD)0.11428571
Skewness0.87237765
Sum3113.0714
Variance0.031247766
MonotonicityNot monotonic
2023-12-05T20:32:48.748295image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.442857143 222
 
4.2%
0.678571429 190
 
3.6%
0.642857143 183
 
3.4%
0.671428571 173
 
3.2%
0.514285714 172
 
3.2%
0.542857143 170
 
3.2%
0.485714286 167
 
3.1%
0.614285714 153
 
2.9%
0.428571429 153
 
2.9%
0.45 147
 
2.8%
Other values (57) 3599
67.5%
ValueCountFrequency (%)
0.192857143 73
1.4%
0.235714286 1
 
< 0.1%
0.285714286 73
1.4%
0.321428571 73
1.4%
0.328571429 8
 
0.2%
0.342857143 23
 
0.4%
0.35 13
 
0.2%
0.371428571 28
 
0.5%
0.378571429 97
1.8%
0.385714286 115
2.2%
ValueCountFrequency (%)
1.192857143 73
1.4%
1.085714286 73
1.4%
0.985714286 73
1.4%
0.964285714 73
1.4%
0.871428571 4
 
0.1%
0.857142857 50
0.9%
0.842857143 73
1.4%
0.821428571 3
 
0.1%
0.8 73
1.4%
0.792857143 66
1.2%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
cubic
3253 
orthorhombic
1573 
rhombohedral
 
323
tetragonal
 
127
-
 
53

Length

Max length12
Median length5
Mean length7.5699005
Min length1

Characters and Unicode

Total characters40340
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcubic
2nd roworthorhombic
3rd rowcubic
4th roworthorhombic
5th roworthorhombic

Common Values

ValueCountFrequency (%)
cubic 3253
61.0%
orthorhombic 1573
29.5%
rhombohedral 323
 
6.1%
tetragonal 127
 
2.4%
- 53
 
1.0%

Length

2023-12-05T20:32:48.846027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T20:32:48.929287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
cubic 3253
61.0%
orthorhombic 1573
29.5%
rhombohedral 323
 
6.1%
tetragonal 127
 
2.4%
53
 
1.0%

Most occurring characters

ValueCountFrequency (%)
c 8079
20.0%
o 5492
13.6%
b 5149
12.8%
i 4826
12.0%
r 3919
9.7%
h 3792
9.4%
u 3253
8.1%
m 1896
 
4.7%
t 1827
 
4.5%
a 577
 
1.4%
Other values (6) 1530
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40287
99.9%
Dash Punctuation 53
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 8079
20.1%
o 5492
13.6%
b 5149
12.8%
i 4826
12.0%
r 3919
9.7%
h 3792
9.4%
u 3253
8.1%
m 1896
 
4.7%
t 1827
 
4.5%
a 577
 
1.4%
Other values (5) 1477
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40287
99.9%
Common 53
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 8079
20.1%
o 5492
13.6%
b 5149
12.8%
i 4826
12.0%
r 3919
9.7%
h 3792
9.4%
u 3253
8.1%
m 1896
 
4.7%
t 1827
 
4.5%
a 577
 
1.4%
Other values (5) 1477
 
3.7%
Common
ValueCountFrequency (%)
- 53
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 8079
20.0%
o 5492
13.6%
b 5149
12.8%
i 4826
12.0%
r 3919
9.7%
h 3792
9.4%
u 3253
8.1%
m 1896
 
4.7%
t 1827
 
4.5%
a 577
 
1.4%
Other values (6) 1530
 
3.8%

Interactions

2023-12-05T20:32:43.397357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:36.257005image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:37.088845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:37.812074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:38.558873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:39.346698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:40.170381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:41.049084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:41.908423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:42.649105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:43.474756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:36.334925image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:37.164970image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:37.886788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:38.642411image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:39.426526image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:40.258463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:41.142203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:41.986768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:42.730943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:43.551358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:36.410128image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:37.240065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:37.959578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:38.728661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:39.514602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:40.338035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:41.219705image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:42.064095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:42.801665image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:43.628958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:36.484851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:37.312498image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:38.034060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:38.812340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:39.596740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:40.419817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:41.298195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:42.135655image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:42.874855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:43.704552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:36.655916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:37.386818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:38.106906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:38.889996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:39.675993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:40.503733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:41.372634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:42.208414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:42.950186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:43.778049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:36.733991image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:37.461289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:38.181469image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:38.971556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:39.752297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:40.582243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:41.503832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:42.293779image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:43.034751image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:43.851931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:36.807601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:37.532257image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:38.254959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:39.049232image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:39.832524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:40.656918image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:41.584660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:42.376743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:43.110421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:43.926629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:36.880471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:37.605431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:38.335695image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:39.123775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:39.910951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:40.729737image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:41.669119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:42.453913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:43.182693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:44.001041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:36.950042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:37.673148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:38.406884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:39.195862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:39.979760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:40.805571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:41.741850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:42.514849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:43.255322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:44.078645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:37.018962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:37.742571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:38.482875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:39.272718image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:40.052032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:40.883198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:41.828793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:42.581585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:32:43.325961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-05T20:32:49.099132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
EN(A)EN(B)In literatureLowest distortionl(A-O)(Å)l(B-O)(Å)r(AVI)(Å)r(AXII)(Å)r(BVI)(Å)tGv(A)v(B)ΔENRμ
EN(A)1.0000.0000.2240.165-0.4810.000-0.653-0.654-0.005-0.5140.1930.1930.420-0.005
EN(B)0.0001.0000.2080.1350.000-0.4170.0000.014-0.6440.3870.2270.2270.687-0.644
In literature0.2240.2081.0000.2470.213-0.1190.1920.201-0.1570.2500.2570.257-0.002-0.157
Lowest distortion0.1650.1350.2471.0000.1780.0540.2320.2360.0590.1560.0900.090-0.1810.059
l(A-O)(Ã…)-0.4810.0000.2130.1781.0000.0000.7440.784-0.0010.6270.2510.251-0.357-0.001
l(B-O)(Ã…)0.000-0.417-0.1190.0540.0001.000-0.000-0.0090.699-0.4160.2930.293-0.5710.699
r(AVI)(Ã…)-0.6530.0000.1920.2320.744-0.0001.0000.9430.0020.7410.1610.161-0.4420.002
r(AXII)(Ã…)-0.6540.0140.2010.2360.784-0.0090.9431.000-0.0120.7940.2010.201-0.451-0.012
r(BVI)(Ã…)-0.005-0.644-0.1570.059-0.0010.6990.002-0.0121.000-0.5750.2230.223-0.8281.000
tG-0.5140.3870.2500.1560.627-0.4160.7410.794-0.5751.0000.1570.1570.113-0.575
v(A)0.1930.2270.2570.0900.2510.2930.1610.2010.2230.1571.0001.0000.327-0.352
v(B)0.1930.2270.2570.0900.2510.2930.1610.2010.2230.1571.0001.0000.390-0.444
ΔENR0.4200.687-0.002-0.181-0.357-0.571-0.442-0.451-0.8280.1130.3270.3901.000-0.828
μ-0.005-0.644-0.1570.059-0.0010.6990.002-0.0121.000-0.575-0.352-0.444-0.8281.000

Missing values

2023-12-05T20:32:44.190715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-05T20:32:44.377362image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CompoundABIn literaturev(A)v(B)r(AXII)(Å)r(AVI)(Å)r(BVI)(Å)EN(A)EN(B)l(A-O)(Å)l(B-O)(Å)ΔENRtGτμLowest distortion
0Ac2O3AcAcFalse001.121.121.121.11.100.00.000000-3.2480000.707107-0.800000cubic
1AcAgO3AcAgFalse001.121.120.951.11.930.02.488353-2.5650710.758259-0.678571orthorhombic
2AcAlO3AcAlFalse001.121.120.541.11.610.01.892894-1.8467140.918510-0.385714cubic
3AcAsO3AcAsFalse001.121.120.521.12.180.01.932227-1.5774290.928078-0.371429orthorhombic
4AcAuO3AcAuFalse001.121.120.931.12.540.02.313698-2.2797860.764768-0.664286orthorhombic
5AcBO3AcBFalse001.121.120.271.12.040.01.624662-1.1173571.067011-0.192857cubic
6AcBaO3AcBaFalse001.121.121.351.10.890.02.640094-3.8067860.647967-0.964286orthorhombic
7AcBeO3AcBeFalse001.121.120.451.11.570.01.803349-1.6769290.963194-0.321429cubic
8AcBiO3AcBiFalse001.121.120.901.12.020.02.215655-2.4258570.774743-0.642857orthorhombic
9AcCaO3AcCaFalse001.121.121.001.11.000.02.318139-3.0402860.742462-0.714286orthorhombic
CompoundABIn literaturev(A)v(B)r(AXII)(Å)r(AVI)(Å)r(BVI)(Å)EN(A)EN(B)l(A-O)(Å)l(B-O)(Å)ΔENRtGτμLowest distortion
5319ZrTiO3ZrTiFalse240.890.720.611.331.542.383421.927849-1.8130360.8056096.0195986780.435714cubic
5320ZrTlO3ZrTlFalse--0.890.721.201.331.622.383422.297684-2.9570710.622798-0.857143orthorhombic
5321ZrTmO3ZrTmFalse--0.890.720.961.331.252.383422.119701-2.5989640.686133-0.685714cubic
5322ZrUO3ZrUFalse150.890.720.761.331.382.383422.047800-2.1612140.7496648.2583824280.542857orthorhombic
5323ZrVO3ZrVFalse150.890.720.541.331.632.383421.758039-1.6456790.8346784.8911825760.385714cubic
5324ZrWO3ZrWFalse150.890.720.621.332.362.383421.745600-1.5722140.8016215.2289524550.442857cubic
5325ZrYO3ZrYFalse--0.890.720.901.331.222.383422.235124-2.4895710.704032-0.642857cubic
5326ZrYbO3ZrYbFalse--0.890.720.951.331.102.383422.223981-2.6268210.689053-0.678571orthorhombic
5327ZrZnO3ZrZnFalse--0.890.720.741.331.652.383422.096141-2.0357500.756670-0.528571cubic
5328Zr2O3ZrZrFalse--0.890.720.721.331.332.383422.043778-2.0978210.763809-0.514286cubic